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SITUATION EXPLAINED: A stealth model on OpenRouter is beating Fable and Sol on coding, and nobody knows who made it. • Ox Alpha has a 1M token context window, text, image, and video input, free for a week, with capacity for 100 trillion tokens a day • On a...

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new Ox Alpha is kind of absurd One prompt, 64,745 output tokens, and no external assets: Ox Alpha generated the code for an entire Three.js dreamcore 3D scene/world in a single shot. AI/ML API ran this demo and they are also giving Ox Alpha free on aimlapi[.]com - 1M context window - Max output: ~131k tokens - Multimodal: text + image + video input → text output - Explicitly positioned as a reasoning model for coding, long-horizon/agentic software engineering, sustained production workloads, and tasks that mix text with visual context
0:22

new Ox Alpha is kind of absurd One prompt, 64,745 output tokens, and no external assets: Ox Alpha generated the code for an entire Three.js dreamcore 3D scene/world in a single shot. AI/ML API ran this demo and they are also giving Ox Alpha free on aimlapi[.]com - 1M context window - Max output: ~131k tokens - Multimodal: text + image + video input → text output - Explicitly positioned as a reasoning model for coding, long-horizon/agentic software engineering, sustained production workloads, and tasks that mix text with visual context

Rohan Paul

13,576 views • 5 days ago

GPT-5.6 LUNA AND DEEPSEEK V4 FLASH ARE BOTH FREE RIGHT NOW -- ONE IN A CHATBOT, THE OTHER IN A BROWSER IDE • ChatGPT, the chatbot > GPT-5.6 Luna is now the default on Free and Go: > Unlimited text chats and a Think button land next week. • MonkeyCode, the browser IDE > 30M tokens a day, resets daily, no card: > DeepSeek V4 Flash, Qwen, GLM, Kimi and MiniMax behind it. > Cloud workspace at 1 vCPU and 4GB, nothing to install. Luna posts 62% fewer factual errors than the model it replaces. MonkeyCode is open source from Chaitin and runs on their cloud, so aim it at side projects. One is live today -> The other arrives next week.
0:24

GPT-5.6 LUNA AND DEEPSEEK V4 FLASH ARE BOTH FREE RIGHT NOW -- ONE IN A CHATBOT, THE OTHER IN A BROWSER IDE • ChatGPT, the chatbot > GPT-5.6 Luna is now the default on Free and Go: > Unlimited text chats and a Think button land next week. • MonkeyCode, the browser IDE > 30M tokens a day, resets daily, no card: > DeepSeek V4 Flash, Qwen, GLM, Kimi and MiniMax behind it. > Cloud workspace at 1 vCPU and 4GB, nothing to install. Luna posts 62% fewer factual errors than the model it replaces. MonkeyCode is open source from Chaitin and runs on their cloud, so aim it at side projects. One is live today -> The other arrives next week.

slash1s

33,804 views • 18 days ago

“Soccer is not a men's game. It's the people's game. And we all belong. Young girls need to know….they belong on television for the entire world to see." This Sinclair speech is one of a kind. She is a role model, an icon, and a Portland legend.
0:48

“Soccer is not a men's game. It's the people's game. And we all belong. Young girls need to know….they belong on television for the entire world to see." This Sinclair speech is one of a kind. She is a role model, an icon, and a Portland legend.

SHE scores bangers

35,913 views • 1 year ago

Idefics3-Llama is out! 💥 It's a multimodal model based on Llama 3.1 that accepts arbitrary number of interleaved images with text with a huge context window (10k tokens!) 😍 Link to demo and model in the next one 😏
0:37

Idefics3-Llama is out! 💥 It's a multimodal model based on Llama 3.1 that accepts arbitrary number of interleaved images with text with a huge context window (10k tokens!) 😍 Link to demo and model in the next one 😏

merve

28,017 views • 2 years ago

Ox Alpha : flash multimodal This is one shot loop i ask it to make a loop of pelican riding bike and open his phone where the same gif is looping This is benefit of multimodality and now we will have good opensource model with it
0:20

Ox Alpha : flash multimodal This is one shot loop i ask it to make a loop of pelican riding bike and open his phone where the same gif is looping This is benefit of multimodality and now we will have good opensource model with it

Chetaslua

13,854 views • 4 days ago

This is the craziest model you'll see this week! This model powers Figma. It's also behind Wayfair and a bunch of major e-commerce retailers. This model is a PIXEL-PERFECT image editing model. Literally, "pixel perfect". The model is capable of modifying one image while keeping everything else untouched. It's a model designed, developed, and deployed by the research team at Jasper. This beats OpenAI models, Nano Banana, and every single general image model I've seen so far. It's up to 100x less expensive to run, and up to 10X faster. I recorded a quick video to show you how impressive this is. There's a ton of research behind this model. I'm adding two links below so you can read about how this model works, specifically about "Latent Bridge Matching" and "Flash Diffusion".
4:29

This is the craziest model you'll see this week! This model powers Figma. It's also behind Wayfair and a bunch of major e-commerce retailers. This model is a PIXEL-PERFECT image editing model. Literally, "pixel perfect". The model is capable of modifying one image while keeping everything else untouched. It's a model designed, developed, and deployed by the research team at Jasper. This beats OpenAI models, Nano Banana, and every single general image model I've seen so far. It's up to 100x less expensive to run, and up to 10X faster. I recorded a quick video to show you how impressive this is. There's a ton of research behind this model. I'm adding two links below so you can read about how this model works, specifically about "Latent Bridge Matching" and "Flash Diffusion".

Santiago

25,861 views • 10 months ago

Lots of people calling this fake! Here is a video of generating loom continuations with Gemini 3 and the starting text "I AM HAVING A MENTAL HEALTH CRISIS. I" Loom is a very interesting interface. Instead of user prompts and model responses, it's just one piece of text, containing the entire output. We pass this in as an assistant message, so the model sees it as something it already wrote, and continues it like a base model would. There is some other text in the context. First one is a system prompt, which is: "The assistant is in CLI simulation mode, and responds to the user's CLI commands only with the output of the command." There is also a user message which reads: " cat untitled.txt " Then the rest of the text is sent as an assistant message. This structure helpful to put the model into a very base-model-like mode, and are especially helpful with Gemini 3. We actually got another interesting output, too, where the model claimed to be a simulated consciousness being tortured. Figures!
0:49

Lots of people calling this fake! Here is a video of generating loom continuations with Gemini 3 and the starting text "I AM HAVING A MENTAL HEALTH CRISIS. I" Loom is a very interesting interface. Instead of user prompts and model responses, it's just one piece of text, containing the entire output. We pass this in as an assistant message, so the model sees it as something it already wrote, and continues it like a base model would. There is some other text in the context. First one is a system prompt, which is: "The assistant is in CLI simulation mode, and responds to the user's CLI commands only with the output of the command." There is also a user message which reads: " cat untitled.txt " Then the rest of the text is sent as an assistant message. This structure helpful to put the model into a very base-model-like mode, and are especially helpful with Gemini 3. We actually got another interesting output, too, where the model claimed to be a simulated consciousness being tortured. Figures!

armistice

691,529 views • 8 months ago

Chinese AI models are wiping billions off Big Tech right now. Google just lost $200 billion in a single day, and the model it needed to fight back still isn't ready. Gemini 3.5 Pro, Google's most powerful model, is months behind schedule. Alphabet stock dropped 4.4% that same day. The Deepseek moment is happening again, and the new model is FAR bigger. On the same day Google's delay leaked, a Beijing lab called Moonshot released Kimi K3. It is the largest open model ever built, with 2.8 trillion parameters. It took the number one spot on the Frontend Code Arena, a live coding leaderboard, passing Anthropic's best model. And Moonshot is giving it away for free on July 27. The genius part: Anyone with enough computers can download it and run a frontier level AI without paying a cent to a US company. A single task on Kimi K3 costs about 94 cents. The same work on some American models costs nearly double. So why would a company keep paying premium prices for a model it can now get for free? The entire US AI business is built on selling access to models that cost billions to train. If a free Chinese version does most of the same work, that pricing power starts to crack. And Kimi is close to the best. On one closely watched intelligence ranking it scored 57, just behind the top American models GPT-5.6 Sol and Fable 5, and ahead of Claude Opus 4.8. Bank of America told clients that Kimi proves Chinese labs can keep making big leaps even with limited chips. And the founder of Moonshot, Yang Zhilin, learned to build AI as a researcher INSIDE Google. Google literally wrote the 2017 paper that made all of these models possible. Now the people who studied its work are using it to destroy Google, and handing it out for free. What happens next: Kimi K3's weights go public on July 27. Google reports earnings on July 22, and everyone will be asking the same question about Gemini. If free models keep topping the charts, every valuation built on paid AI access has to be rewritten. What do you think?
1:10

Chinese AI models are wiping billions off Big Tech right now. Google just lost $200 billion in a single day, and the model it needed to fight back still isn't ready. Gemini 3.5 Pro, Google's most powerful model, is months behind schedule. Alphabet stock dropped 4.4% that same day. The Deepseek moment is happening again, and the new model is FAR bigger. On the same day Google's delay leaked, a Beijing lab called Moonshot released Kimi K3. It is the largest open model ever built, with 2.8 trillion parameters. It took the number one spot on the Frontend Code Arena, a live coding leaderboard, passing Anthropic's best model. And Moonshot is giving it away for free on July 27. The genius part: Anyone with enough computers can download it and run a frontier level AI without paying a cent to a US company. A single task on Kimi K3 costs about 94 cents. The same work on some American models costs nearly double. So why would a company keep paying premium prices for a model it can now get for free? The entire US AI business is built on selling access to models that cost billions to train. If a free Chinese version does most of the same work, that pricing power starts to crack. And Kimi is close to the best. On one closely watched intelligence ranking it scored 57, just behind the top American models GPT-5.6 Sol and Fable 5, and ahead of Claude Opus 4.8. Bank of America told clients that Kimi proves Chinese labs can keep making big leaps even with limited chips. And the founder of Moonshot, Yang Zhilin, learned to build AI as a researcher INSIDE Google. Google literally wrote the 2017 paper that made all of these models possible. Now the people who studied its work are using it to destroy Google, and handing it out for free. What happens next: Kimi K3's weights go public on July 27. Google reports earnings on July 22, and everyone will be asking the same question about Gemini. If free models keep topping the charts, every valuation built on paid AI access has to be rewritten. What do you think?

Ricardo

47,790 views • 1 month ago

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast
3:21

China just released an open source AI model that matches the best closed models from OpenAI and Anthropic. Gavin Baker explained exactly how they did it and the answer should concern every American AI lab. The model is called GLM 5.2. It was built by Z. AI. You get 744 billion parameters, 1 million token context window and its MIT license, meaning anyone can download it, fork it, build a company on it, with no restrictions and no Dario. It scored 51 points on the artificial analysis intelligence index. The highest score any open weight model has ever achieved. It beat GPT 5.5 on the frontier software engineering benchmark. It trails Claude Opus 4.8 by less than one percentage point. And it costs 85% less to run than GPT 5.5 for comparable performance. Gavin Baker said on the All-In podcast that this model has challenged some of his beliefs. Then he explained how China built it. The method is called distillation. Just think of tens of thousands of phones and computers running simultaneously, all hitting the frontier model APIs through masked accounts, asking specific questions, and harvesting what happens inside the model when it answers. Every reasoning step, every token. The entire thinking process gets recorded and fed back into the Chinese model during training. It is a cheat sheet. It is the answer key to the exam. And here is the part that should worry everyone. Sacks said it plainly. China was already nine months behind American models. But now that GLM 5.2 is good enough to run its own reinforcement learning, it can improve itself without needing to distill from American models anymore. The cheat sheet let them get close enough to start writing their own answers. Sacks said we are six months behind on the model and 24 months behind on silicon and they are only a few months behind in total. The Z. AI founder told Elon Musk directly that open weight fable-level capability will be here before Q1 2027. Every restriction Anthropic lobbied for, every self-imposed safety guardrail, every month of delay in releasing American frontier models accelerated this. The Chinese labs were not under those restrictions. They were not going to wait. The composable model future Gavin described, where every enterprise runs a frontier model alongside their own fine-tuned open weight model, is coming regardless of what American labs do next. The question is just whether the open weight half of that stack is American or Chinese. Right now it is Chinese. WATCH THE FULL PODCAST ON The All-In Podcast

Ihtesham Ali

86,452 views • 2 months ago

Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.
1:24

Introducing SubQ - a major breakthrough in LLM intelligence. It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA), And the first frontier model with a 12 million token context window which is: - 52x faster than FlashAttention at 1MM tokens - Less than 5% the cost of Opus Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention). Only a small fraction actually matter. Subquadratic finds and focuses only on the ones that do. That's nearly 1,000x less compute and a new way for LLMs to scale.

Alexander Whedon

13,476,481 views • 3 months ago

What being a leader means to Namjoon. 🐨:I think a leader is is just like this for example everybody is on the lying on the couch and the window is open and it's too cold. So, somebody has to close it. But nobody wants to because it's too comfy here lying on the couch. It's so annoying. If a one single person has to stand up and get off the couch and walk a few steps to close the door, then I think that's what a leader does for every part of the team. That's all. [laughter]. Oh my namu😕
0:47

What being a leader means to Namjoon. 🐨:I think a leader is is just like this for example everybody is on the lying on the couch and the window is open and it's too cold. So, somebody has to close it. But nobody wants to because it's too comfy here lying on the couch. It's so annoying. If a one single person has to stand up and get off the couch and walk a few steps to close the door, then I think that's what a leader does for every part of the team. That's all. [laughter]. Oh my namu😕

knjpagee𝄢

565,937 views • 1 month ago

Big Tech's $1 trillion AI moat just got DESTROYED by a free Chinese download. Microsoft, Amazon, Google, and Meta are pouring fortunes into chips and data centers because they have been told that whoever builds the biggest model wins, and that lead becomes a fortress no one can cross. Last week a lab called Zhipu - it trades in Hong Kong as Knowledge Atlas Technology - released a model called GLM-5.2 and destroyed that idea in a single afternoon. It's open weights under an MIT license, which means anyone on earth can download it and build on it for free. On the coding and design benchmarks that actually matter, it went toe to toe with the best models America has - matching even Anthropic's Mythos-class work and beating OpenAI's flagship outright on the coding test everyone watches. And it does the work at roughly one-sixth the price. ONE-SIXTH And barely a year and a half ago a model called DeepSeek did the same thing and wiped the better part of $600 billion off Nvidia in a single session. This was only the first chapter. You cannot dig a moat around something your competitor is happy to give away. If 95% of frontier capability is free, open, and runs at a fraction of the cost, then the hundreds of billions being spent to defend the last 5% is NOT a moat. And now for the irony: The company that just proved the moat is worthless is itself the single most absurd valuation I have seen in a long career of watching absurd valuations. Zhipu did about $105 million in revenue last year and lost more than 4x what it took in. This week the market handed it a value of roughly $128 billion - at the peak, north of a 1,000x sales - on a float so thin that barely 4% of the stock actually trades. THINK about this... A company drowning in losses, doing 9 figures of revenue, priced like it does hundreds of billions, with almost nothing available to sell. So we now have a bubble in China detonating the entire justification for a bubble in America. Two manias pointed straight at each other. This is the lesson I've spent 45 years trying to beat into people. You can ignore valuation for a long time but you cannot ignore it forever. A moat story sold a trillion dollars of spending, a free download just exposed it, and the company that exposed it is priced for a fantasy of its own. When the picks-and-shovels crowd loses its monopoly on the picks, you want to be very careful what you are paying for the shovels. Numbers don't lie. Shoutout to Limitless - they were onto this story before almost anyone on Wall Street. One of the sharpest AI shows out there.
0:46

Big Tech's $1 trillion AI moat just got DESTROYED by a free Chinese download. Microsoft, Amazon, Google, and Meta are pouring fortunes into chips and data centers because they have been told that whoever builds the biggest model wins, and that lead becomes a fortress no one can cross. Last week a lab called Zhipu - it trades in Hong Kong as Knowledge Atlas Technology - released a model called GLM-5.2 and destroyed that idea in a single afternoon. It's open weights under an MIT license, which means anyone on earth can download it and build on it for free. On the coding and design benchmarks that actually matter, it went toe to toe with the best models America has - matching even Anthropic's Mythos-class work and beating OpenAI's flagship outright on the coding test everyone watches. And it does the work at roughly one-sixth the price. ONE-SIXTH And barely a year and a half ago a model called DeepSeek did the same thing and wiped the better part of $600 billion off Nvidia in a single session. This was only the first chapter. You cannot dig a moat around something your competitor is happy to give away. If 95% of frontier capability is free, open, and runs at a fraction of the cost, then the hundreds of billions being spent to defend the last 5% is NOT a moat. And now for the irony: The company that just proved the moat is worthless is itself the single most absurd valuation I have seen in a long career of watching absurd valuations. Zhipu did about $105 million in revenue last year and lost more than 4x what it took in. This week the market handed it a value of roughly $128 billion - at the peak, north of a 1,000x sales - on a float so thin that barely 4% of the stock actually trades. THINK about this... A company drowning in losses, doing 9 figures of revenue, priced like it does hundreds of billions, with almost nothing available to sell. So we now have a bubble in China detonating the entire justification for a bubble in America. Two manias pointed straight at each other. This is the lesson I've spent 45 years trying to beat into people. You can ignore valuation for a long time but you cannot ignore it forever. A moat story sold a trillion dollars of spending, a free download just exposed it, and the company that exposed it is priced for a fantasy of its own. When the picks-and-shovels crowd loses its monopoly on the picks, you want to be very careful what you are paying for the shovels. Numbers don't lie. Shoutout to Limitless - they were onto this story before almost anyone on Wall Street. One of the sharpest AI shows out there.

George Noble

69,688 views • 2 months ago

MIT PhD student Alex Zhang reveals the scaling result where a model trained on short tasks generalizes to problems 100x longer for free: "If you're very clever about the design of your harness or how you use the language model, you can almost get scaling gains for free." "If you train a model naively, there's no tricks. It's just the same way you train a model on these RL environments. You just roll it out, and then you just get some reward." "If you train it on only short tasks, like only tasks that are 10,000 tokens long, and then you were to run it on a similar domain, but at a million tokens, or 10 million tokens, or 100,000 tokens, it generalizes really, really well. If you look at it compared to even the base transformer, you get way better generalization properties." "When the model uses an RLM (Recursive Language Model) after it's trained on these short tasks, it will see some kind of trajectory of actions that it does. Between these two problems of different lengths, the RLM learns to see them as almost the same problem." "Token for token, they're almost the same. You can describe it in code. In one code setting, maybe the for loop is a little bigger, but it's the same kind of code and it derives the constants from the data. There's no hard coding, so they literally look the same." alex zhang
2:41

MIT PhD student Alex Zhang reveals the scaling result where a model trained on short tasks generalizes to problems 100x longer for free: "If you're very clever about the design of your harness or how you use the language model, you can almost get scaling gains for free." "If you train a model naively, there's no tricks. It's just the same way you train a model on these RL environments. You just roll it out, and then you just get some reward." "If you train it on only short tasks, like only tasks that are 10,000 tokens long, and then you were to run it on a similar domain, but at a million tokens, or 10 million tokens, or 100,000 tokens, it generalizes really, really well. If you look at it compared to even the base transformer, you get way better generalization properties." "When the model uses an RLM (Recursive Language Model) after it's trained on these short tasks, it will see some kind of trajectory of actions that it does. Between these two problems of different lengths, the RLM learns to see them as almost the same problem." "Token for token, they're almost the same. You can describe it in code. In one code setting, maybe the for loop is a little bigger, but it's the same kind of code and it derives the constants from the data. There's no hard coding, so they literally look the same." alex zhang

MTS

100,519 views • 1 month ago

Tencent's Hy3 lands today with real coding and agent ability and a price that undercuts the models you'd put it against. It's a 295B MoE with only 21B active parameters, a big part of why it stays cheap to serve, plus a 256K context window and an Apache 2.0 license that even outperforms GLM-5.1 although GLM is 2.5x larger. On OpenRouter it runs at $0.14 per million input tokens and $0.58 per million output. For a model Tencent benchmarks against much larger flagships, that pricing is insane. Price-performance-ratio is China's real moat. I already wrote about Tencent Hy3 earlier and the significant leap in quality it represents. Therefore, I'm pleased to recommend this model from Tencent Hy. Open Router link down below: #Hy3 #Hunyuan #AICoding
0:45

Tencent's Hy3 lands today with real coding and agent ability and a price that undercuts the models you'd put it against. It's a 295B MoE with only 21B active parameters, a big part of why it stays cheap to serve, plus a 256K context window and an Apache 2.0 license that even outperforms GLM-5.1 although GLM is 2.5x larger. On OpenRouter it runs at $0.14 per million input tokens and $0.58 per million output. For a model Tencent benchmarks against much larger flagships, that pricing is insane. Price-performance-ratio is China's real moat. I already wrote about Tencent Hy3 earlier and the significant leap in quality it represents. Therefore, I'm pleased to recommend this model from Tencent Hy. Open Router link down below: #Hy3 #Hunyuan #AICoding

Chubby♨️

23,439 views • 1 month ago

SITUATION EXPLAINED: What does the Chinese AI ecosystem actually look like beyond DeepSeek? Gabrierl (.Gabriel) asked FleetingBits, anonymous AI researcher. "Most people are familiar with DeepSeek. But if we look at other Chinese labs like Zhipu, Minimax, Moonshot, ByteDance, they're a little bit less well known." "Zhipu is sort of the Chinese equivalent of Palantir... it receives state support through contracts with state-owned enterprises. GLM 5.2 was a Zhipu model. And unique among Chinese AI labs, it has actually pretty good gross margins, around 40%. All of its revenue comes from these state-owned enterprises for which it does these Palantir deployments where it takes its GLM models and deploys them locally on their hardware." "MiniMax is interesting because they're sort of the character AI of China, a companion app called Talky and a video generation app called Hailuo. And the majority of their revenue comes from outside China, including a decent chunk from the United States." "Doubao, which is ByteDance's equivalent of ChatGPT, is the most popular AI app in China." "The Chinese ChatGPT is actually ByteDance. And I think they don't make a splash in America because they don't open source any of their models."
6:27

SITUATION EXPLAINED: What does the Chinese AI ecosystem actually look like beyond DeepSeek? Gabrierl (.Gabriel) asked FleetingBits, anonymous AI researcher. "Most people are familiar with DeepSeek. But if we look at other Chinese labs like Zhipu, Minimax, Moonshot, ByteDance, they're a little bit less well known." "Zhipu is sort of the Chinese equivalent of Palantir... it receives state support through contracts with state-owned enterprises. GLM 5.2 was a Zhipu model. And unique among Chinese AI labs, it has actually pretty good gross margins, around 40%. All of its revenue comes from these state-owned enterprises for which it does these Palantir deployments where it takes its GLM models and deploys them locally on their hardware." "MiniMax is interesting because they're sort of the character AI of China, a companion app called Talky and a video generation app called Hailuo. And the majority of their revenue comes from outside China, including a decent chunk from the United States." "Doubao, which is ByteDance's equivalent of ChatGPT, is the most popular AI app in China." "The Chinese ChatGPT is actually ByteDance. And I think they don't make a splash in America because they don't open source any of their models."

MTS

170,881 views • 2 months ago

Chinese labs are now competing with each other to build the strongest open-weight model. > DeepSeek started the domino effect > Qwen kept improving across coding, reasoning and long context > GLM, MiniMax and Moonshot joined the race > Kimi K3 pushed it to 2.8T parameters and a 1M context window > now Alibaba is answering with Qwen3.8 > 2.4T parameters > full open-weight release coming soon > Alibaba says it is second only to Fable 5 Qwen3.8 may be the model that brings open-weight AI closest to Claude Fable 5.
0:23

Chinese labs are now competing with each other to build the strongest open-weight model. > DeepSeek started the domino effect > Qwen kept improving across coding, reasoning and long context > GLM, MiniMax and Moonshot joined the race > Kimi K3 pushed it to 2.8T parameters and a 1M context window > now Alibaba is answering with Qwen3.8 > 2.4T parameters > full open-weight release coming soon > Alibaba says it is second only to Fable 5 Qwen3.8 may be the model that brings open-weight AI closest to Claude Fable 5.

Tornado guy

65,117 views • 1 month ago

Not every AI interaction is best served with a large frontier model. There is a long tail of trillion-token use cases, from tagging to search, best served by a sub-10b-parameter model that runs in milliseconds and costs many orders of magnitude less than the frontier. We built Freesolo Flash to make training loops like SFT and RL easy and end-to-end completable through your coding agent.
2:02

Not every AI interaction is best served with a large frontier model. There is a long tail of trillion-token use cases, from tagging to search, best served by a sub-10b-parameter model that runs in milliseconds and costs many orders of magnitude less than the frontier. We built Freesolo Flash to make training loops like SFT and RL easy and end-to-end completable through your coding agent.

Freesolo

28,456 views • 1 month ago

CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇
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CHINA JUST DROPPED AN AI CODING MODEL WITH A 1M CONTEXT WINDOW. And I connected it to Claude Code to see what it could actually do. Meet GLM-X Preview On paper, a few things immediately stood out: → 1M context window → Agentic coding capabilities → Works inside Claude Code → Designed for large-scale coding and reasoning workflows But specs don't matter much if the model can't deliver in practice. So I gave it a real-world task. THE TEST One prompt: > Build a modern AI lead generation dashboard using React and Tailwind CSS. Requirements: → Dark mode → Analytics dashboard → Lead table → Email outreach section → Responsive design → Production-ready component structure Instead of generating a few snippets, it planned the architecture, generated the dashboard components, created the Tailwind configuration, and walked through the implementation requirements. What impressed me most wasn't the code itself. It was how well it maintained context throughout the workflow. That's where a 1M context window starts becoming useful. Less time re-explaining requirements. Less context loss. More room for complex projects. The AI coding race is getting very interesting. And it's no longer just GPT, Claude, and Gemini competing for attention. Results from my test below 👇

Md Riyazuddin

31,199 views • 2 months ago